Many real world systems can be described by a set of differential equations. Knowing these equations allows researchers to predict the system's behaviour under interventions, such as manipulations of initial or environmental conditions. For many complex systems, the differential equations are unknown. Deriving them by hand is infeasible for large systems, and data science is used to learn them from observational data. Existing techniques yield models that predict the observational data well, but fail to explain the effect of interventions. CausalKinetiX is a methodology for inferring the structure of kinetic systems by explicitly taking into account stability across different experiments. This allows to draw a more realistic picture of the system's underlying causal structure and is a first step towards increasing reproducibility.
The CausalKinetiX framework is described in the following paper:
N. Pfister, S. Bauer, J. Peters: *Learning Stable and Predictive Structures in Kinetic Systems*. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.1905688116An open access pre-print version is available here.